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This model puts an emphasis on the zeros in the analysis representation, z, rather then the non-zeros, in characterizing the signal. This is much like the way zero-crossings of wavelets are used to define a signal [Mallat (`91)].

If  is in general position , thenand thus we cannot expect to get a truly sparse analysis representation – Is this a problem? Not necessarily!

In the general case, for d=40 and p=n=80, this graph shows the count of the number of subspaces. As can be seen, the two models are substantially different, the analysis model is much richer in low-dim., and the two complete each other.

The analysis model tends to lead to a richer UoS. Are these good news?